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Decision Support System (DSS) Dashboard
The main issues:
Searching the internet for Business Intelligence (BI) tools, there are big name vendors with brilliant webpages presenting great features and capabilities. This makes anyone wonder if CEO are buying what are posted on these webpages or the claims and the promises posted by these vendors.

The main issues in building BI tools are the size and complexity of Big Data, the ever-changing markets and clients, social media impact on the clients, the ever-changing technologies, and the rest of the list is not small. This boils down to the following:

       Storing and handling Big Data
       Turning Big Data into manageable matrixes for faster and intelligent analysis
       Making sense of Big Data, CRM, Social Media and market-clients trends
       Build a GUI Decision Support System (DSS) Dashboard

Use Case:
              Decision Support System (DSS) Dashboard

Intelligent Dashboards are the latest and the greatest tools used by CEO in making decisions, plans, strategies, budgets and reports. How can we architect an Intelligent Decision Support System (DSS) Dashboard for CEO which provides real answers not just brilliant webpages with claims and promises.

Briefly Describe This Use Case
Big Data (structured and unstructured) is still a puzzle and making sense of Big Data is the goal. Big Data need to be stored, summarized, processed, and analyzed. Data should be presented in an effective format, otherwise, it would not have any value. Our best answer is using matrixes and hash tables. The analogy of our approach is the use of Shorthand or Stenography which increases speed and briefness of writing as compared to longhand. With the same principle, tables and hash tables would be our short hand of values found in Big Data. In short, we would turn data into a tabular format. Tables are best suited for representing individual information and represent both quantitative and qualitative information. Hash Tables can be used as indexes to place holders of more data or processes.

Primary Actor:
CEO, stakeholders, directors and architects.

Dashboard is mainly used as a management tool to gauge performance and progress toward business goals. Dashboards can be designed and developed to address a wide range of objectives, from monitoring the practicality of a global organization's business strategy to deliver a mix of operational, financial and project-specific information.

We are taking dashboard to another intelligent, dynamic and real time level. We architecting dashboard with intelligent layers. Each layer is a service its successor. We also adding website builder features, where CEO and their supportive teams can build a running website with all the site gadgets and products details in real time with a push of a button. See our        Spring Replacement (The Elephant in The Room) Page.

Our analysis in a nutshell is how to shorthand values stored in Big Data into a number of manageable tables and hash tables for faster processes and analysis. These tables must be kept running in memory, updated on a regular basis and stored or backed up for reuse. We need to learn from that fact that Big Data size and complexity are manageable. Our architect would create a number services and each service has a limited number of matrixes and hash tables. We should make use of algorithms and abstract thinking in building the matrixes and the hash tables.

Using Virtualization Objects:
Virtualization means to create a virtual version of a device or resource, such as a server, storage device, network or even an operating system where the framework divides the resource into one or more execution. We are architecting virtual servers and virtual objects as well as virtual testing servers for testing these virtual objects.

Architecting Intelligent Objects:
How can we add Intelligent to Java Objects?
We are implementing matrixes and bit maps for fast processing using numeric values.
These processes include:

Adding steps-code-methods which collect the needed data for processing
Dynamic Business Rules
Dynamic Business rules main job is providing values, processes, sequence of execution and decision-making parameters
Collecting the actual values to be processed
We architecting Engines objects where each engine performs one task (a number of mini-processes). We also architecting Service Objects which use a number of engines objects.
Using Bit maps and number for fast compression
Searching for ranges, limits and max values
Finding patterns and trends
Looking for patterns and trends
Thinking in abstract
From values, ranges, trends, frequencies and other parameters, we can conclude or forecast events or actions
Cross-reference of values would give us the ability to find errors and conclude or forecast events or actions
Statistic Pool
Tracking the success-failure of each processes and object
Decision-making based on dynamic business rules
Use Business Rules and weight-score values to make educated guess or decisions

Our Dashboard architect's main goal is to turn Big Data into data visualizing tool to help CEO compete in today's competitive market. Our architect is to shorthand values in big data into manageable data format. Then use this format in creating CEO Data Visualizer. We are also taking dashboard to another intelligent, dynamic and real time level. CEO and stakeholders and their supportive teams can actually build an entire website with all its gadgets and products details. We using Virtualization in the dashboard implementation.

DSS Dashboard Diagram
DSS Dashboard Diagram - Figure #1

DSS Dashboard Diagram (Figure #1) presents how data flow through the systems and types of processes and services are utilized to create an intelligent DSS Dashboard.

Input of Big Data:
Big Data is composed of Petabytes (1,024 terabytes) or exabytes (1,024 petabytes) of data which consists of billions to trillions of records of millions of people. Structured data is what databases are storing. Unstructured are files of different types of data such as XML, Excel sheets, PDF, images, text, video, audio, ..etc. The size and complexity of data is main issue. The following are some of Big Data input:

       1. History
       2. Market Research
       3. Statistics
       4. Competition
       5. Social Media
       6. Structured Data
       7. Unstructured Data

Virtual Matrixes and Hash tables:
To shorthand values in Big Data, we are proposing the use of the following methodologies. First we need to group data values into target groups such as personal, business, type of business, transactional processes, statistics, ..etc. We can use abstract thinking in making decisions and predications. The data selected for these target groups would be stored in data table and hash tables. We are implementing hash tables as lookup values which may not be numeric. These data tables and hash tables would be running as memory resident objects which can be stored or backed up. Virtualization is an added feature which helps in customization-building-deleting these objects. It can also clones-copies these objects with speed. The following are some of our approaches, methods and processes:

       1. Algorithms
       2. Abstract Thinking
       3. Personal
       4. Business
       5. Big Business
       6. Transactional Processes
       7. Statistics

Virtual Engines Objects:
Once the target data object are created, then we need build what we call Engines. These engines use the target data objects to perform the dashboard detailed tasks or processes. Each engine performs only one task and no overlapping of tasks. These engines should be documented and tested. These engines are also virtual objects. They are loosely coupled and refactoring and transparency are also implemented. The following are engines tasks list:

       1. Social Media
       2. Marketing
       3. Segmentation
       4. Customization
       5. Trends and tendencies
       6. Forecasting
       7. Promotion
       8. Operation
       9. Sales
       10. Risks
       11. Competition
       12. Ad Hoc handling
       13. Strategic Planning
       14. Plans
       15. Finance
       16. Key Performance Indicators
       17. Reports

Virtual Services Objects:
Service Objects would be using different engines objects to create independent services. These services objects would create loosely coupled services. Therefore the details of each services are performed by a number of engines. Overlapping of usage of engines would help eliminate duplication of development code and effort. The following are some of the virtual services objects:

       1. Analytics
       2. Marketing
       3. Trends and tendencies
       4. Forecasting
       5. Promotion
       6. Sales
       7. Competition
       8. Operations
       9. Ad Hoc handling
       10. Strategic Planning
       11. Plans
       12. Finance
       13. Key Performance Indicators
       14. Reports

Virtual Business Objects:
Business Objects are the cloud dashboard services handlers. They would create the services objects to handle the Virtual Cloud based Dashboard requests. They perform the management of services.

Virtual Cloud based Dashboard:
Virtual Cloud based Dashboard is the virtual web cloud server running on the web. It handle security using a number security components including firewalls.

GUI Frontend Data Visualizer:
GUI Frontend Data Visualizer is GUI frontend component which the end users would be using.

Disk Backup:
These can be implemented in a number of ways including Network-Attached Storage (NAS) or and Storage area networks (SANs)

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